--- a/vllm/_custom_ops.py
+++ b/vllm/_custom_ops.py
@@ -1933,14 +1933,28 @@
     if scale is None:
         if use_per_token_if_dynamic:
             scale = torch.empty((shape[0], 1), device=input.device, dtype=torch.float32)
-            torch.ops._C.dynamic_per_token_scaled_fp8_quant(
-                output, input, scale, scale_ub
-            )
+            # COREXX_FP8_QUANT_PATCH: native fallback (vLLM _C op missing on CoreX)
+            m = input.shape[0]
+            s = input.abs().amax(dim=1, keepdim=True).float().clamp_min(1e-12) / 448.0
+            if scale_ub is not None:
+                ub = torch.as_tensor(scale_ub, device=s.device, dtype=s.dtype)
+                s = torch.minimum(s, ub)
+            scale[:m].copy_(s)
+            if shape[0] > m:
+                scale[m:].fill_(1.0)
+                output[m:].zero_()
+            output[:m].copy_((input.float() / s).to(out_dtype))
         else:
             scale = torch.empty(1, device=input.device, dtype=torch.float32)
-            torch.ops._C.dynamic_scaled_fp8_quant(output, input, scale)
+            # COREXX_FP8_QUANT_PATCH: native fallback
+            amax = input.abs().amax().float().clamp_min(1e-12)
+            scale.fill_(amax / 448.0)
+            output.copy_((input.float() / scale).to(out_dtype))
     else:
-        torch.ops._C.static_scaled_fp8_quant(output, input, scale, group_shape)
+        # COREXX_FP8_QUANT_PATCH: native fallback
+        if group_shape is not None and tuple(group_shape) not in ((-1, -1), (-1, 1), (1, -1)):
+            raise NotImplementedError(f"native fp8 static quant: unsupported group_shape {group_shape}")
+        output.copy_((input.float() / scale).to(out_dtype))
 
     return output, scale
 
@@ -3824,6 +3838,9 @@
         mul_routed_weight,
         top_k,
         config['BLOCK_SIZE_M'],
+        # COREXX_FP8_MOE_PATCH: pass quant scales to ixformer kernel
+        w_scale=B_scale,
+        a_scale=A_scale,
         bias=bias
     )